# coding=utf-8
# Copyright 2018 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Auto Model class. """


import warnings
from collections import OrderedDict

from .configuration_auto import (
    AlbertConfig,
    AutoConfig,
    BartConfig,
    BertConfig,
    BertGenerationConfig,
    BlenderbotConfig,
    CamembertConfig,
    CTRLConfig,
    DebertaConfig,
    DistilBertConfig,
    DPRConfig,
    ElectraConfig,
    EncoderDecoderConfig,
    FlaubertConfig,
    FSMTConfig,
    FunnelConfig,
    GPT2Config,
    LayoutLMConfig,
    LongformerConfig,
    LxmertConfig,
    MBartConfig,
    MobileBertConfig,
    OpenAIGPTConfig,
    PegasusConfig,
    ProphetNetConfig,
    ReformerConfig,
    RetriBertConfig,
    RobertaConfig,
    SqueezeBertConfig,
    T5Config,
    TransfoXLConfig,
    XLMConfig,
    XLMProphetNetConfig,
    XLMRobertaConfig,
    XLNetConfig,
    replace_list_option_in_docstrings,
)
from .configuration_marian import MarianConfig
from .configuration_utils import PretrainedConfig
from .file_utils import add_start_docstrings
from .modeling_albert import (
    AlbertForMaskedLM,
    AlbertForMultipleChoice,
    AlbertForPreTraining,
    AlbertForQuestionAnswering,
    AlbertForSequenceClassification,
    AlbertForTokenClassification,
    AlbertModel,
)
from .modeling_bart import (
    BartForConditionalGeneration,
    BartForQuestionAnswering,
    BartForSequenceClassification,
    BartModel,
)
from .modeling_bert import (
    BertForMaskedLM,
    BertForMultipleChoice,
    BertForNextSentencePrediction,
    BertForPreTraining,
    BertForQuestionAnswering,
    BertForSequenceClassification,
    BertForTokenClassification,
    BertLMHeadModel,
    BertModel,
)
from .modeling_bert_generation import BertGenerationDecoder, BertGenerationEncoder
from .modeling_blenderbot import BlenderbotForConditionalGeneration
from .modeling_camembert import (
    CamembertForCausalLM,
    CamembertForMaskedLM,
    CamembertForMultipleChoice,
    CamembertForQuestionAnswering,
    CamembertForSequenceClassification,
    CamembertForTokenClassification,
    CamembertModel,
)
from .modeling_ctrl import CTRLLMHeadModel, CTRLModel
from .modeling_deberta import DebertaForSequenceClassification, DebertaModel
from .modeling_distilbert import (
    DistilBertForMaskedLM,
    DistilBertForMultipleChoice,
    DistilBertForQuestionAnswering,
    DistilBertForSequenceClassification,
    DistilBertForTokenClassification,
    DistilBertModel,
)
from .modeling_dpr import DPRQuestionEncoder
from .modeling_electra import (
    ElectraForMaskedLM,
    ElectraForMultipleChoice,
    ElectraForPreTraining,
    ElectraForQuestionAnswering,
    ElectraForSequenceClassification,
    ElectraForTokenClassification,
    ElectraModel,
)
from .modeling_encoder_decoder import EncoderDecoderModel
from .modeling_flaubert import (
    FlaubertForMultipleChoice,
    FlaubertForQuestionAnsweringSimple,
    FlaubertForSequenceClassification,
    FlaubertForTokenClassification,
    FlaubertModel,
    FlaubertWithLMHeadModel,
)
from .modeling_fsmt import FSMTForConditionalGeneration, FSMTModel
from .modeling_funnel import (
    FunnelForMaskedLM,
    FunnelForMultipleChoice,
    FunnelForPreTraining,
    FunnelForQuestionAnswering,
    FunnelForSequenceClassification,
    FunnelForTokenClassification,
    FunnelModel,
)
from .modeling_gpt2 import GPT2ForSequenceClassification, GPT2LMHeadModel, GPT2Model
from .modeling_layoutlm import LayoutLMForMaskedLM, LayoutLMForTokenClassification, LayoutLMModel
from .modeling_longformer import (
    LongformerForMaskedLM,
    LongformerForMultipleChoice,
    LongformerForQuestionAnswering,
    LongformerForSequenceClassification,
    LongformerForTokenClassification,
    LongformerModel,
)
from .modeling_lxmert import LxmertForPreTraining, LxmertForQuestionAnswering, LxmertModel
from .modeling_marian import MarianMTModel
from .modeling_mbart import MBartForConditionalGeneration
from .modeling_mobilebert import (
    MobileBertForMaskedLM,
    MobileBertForMultipleChoice,
    MobileBertForNextSentencePrediction,
    MobileBertForPreTraining,
    MobileBertForQuestionAnswering,
    MobileBertForSequenceClassification,
    MobileBertForTokenClassification,
    MobileBertModel,
)
from .modeling_openai import OpenAIGPTForSequenceClassification, OpenAIGPTLMHeadModel, OpenAIGPTModel
from .modeling_pegasus import PegasusForConditionalGeneration
from .modeling_prophetnet import ProphetNetForCausalLM, ProphetNetForConditionalGeneration, ProphetNetModel
from .modeling_rag import (  # noqa: F401 - need to import all RagModels to be in globals() function
    RagModel,
    RagSequenceForGeneration,
    RagTokenForGeneration,
)
from .modeling_reformer import (
    ReformerForMaskedLM,
    ReformerForQuestionAnswering,
    ReformerForSequenceClassification,
    ReformerModel,
    ReformerModelWithLMHead,
)
from .modeling_retribert import RetriBertModel
from .modeling_roberta import (
    RobertaForCausalLM,
    RobertaForMaskedLM,
    RobertaForMultipleChoice,
    RobertaForQuestionAnswering,
    RobertaForSequenceClassification,
    RobertaForTokenClassification,
    RobertaModel,
)
from .modeling_squeezebert import (
    SqueezeBertForMaskedLM,
    SqueezeBertForMultipleChoice,
    SqueezeBertForQuestionAnswering,
    SqueezeBertForSequenceClassification,
    SqueezeBertForTokenClassification,
    SqueezeBertModel,
)
from .modeling_t5 import T5ForConditionalGeneration, T5Model
from .modeling_transfo_xl import TransfoXLLMHeadModel, TransfoXLModel
from .modeling_xlm import (
    XLMForMultipleChoice,
    XLMForQuestionAnsweringSimple,
    XLMForSequenceClassification,
    XLMForTokenClassification,
    XLMModel,
    XLMWithLMHeadModel,
)
from .modeling_xlm_prophetnet import (
    XLMProphetNetForCausalLM,
    XLMProphetNetForConditionalGeneration,
    XLMProphetNetModel,
)
from .modeling_xlm_roberta import (
    XLMRobertaForCausalLM,
    XLMRobertaForMaskedLM,
    XLMRobertaForMultipleChoice,
    XLMRobertaForQuestionAnswering,
    XLMRobertaForSequenceClassification,
    XLMRobertaForTokenClassification,
    XLMRobertaModel,
)
from .modeling_xlnet import (
    XLNetForMultipleChoice,
    XLNetForQuestionAnsweringSimple,
    XLNetForSequenceClassification,
    XLNetForTokenClassification,
    XLNetLMHeadModel,
    XLNetModel,
)
from .utils import logging


# Add modeling imports here

logger = logging.get_logger(__name__)


MODEL_MAPPING = OrderedDict(
    [
        # Base model mapping
        (RetriBertConfig, RetriBertModel),
        (T5Config, T5Model),
        (DistilBertConfig, DistilBertModel),
        (AlbertConfig, AlbertModel),
        (CamembertConfig, CamembertModel),
        (XLMRobertaConfig, XLMRobertaModel),
        (BartConfig, BartModel),
        (LongformerConfig, LongformerModel),
        (RobertaConfig, RobertaModel),
        (LayoutLMConfig, LayoutLMModel),
        (SqueezeBertConfig, SqueezeBertModel),
        (BertConfig, BertModel),
        (OpenAIGPTConfig, OpenAIGPTModel),
        (GPT2Config, GPT2Model),
        (MobileBertConfig, MobileBertModel),
        (TransfoXLConfig, TransfoXLModel),
        (XLNetConfig, XLNetModel),
        (FlaubertConfig, FlaubertModel),
        (FSMTConfig, FSMTModel),
        (XLMConfig, XLMModel),
        (CTRLConfig, CTRLModel),
        (ElectraConfig, ElectraModel),
        (ReformerConfig, ReformerModel),
        (FunnelConfig, FunnelModel),
        (LxmertConfig, LxmertModel),
        (BertGenerationConfig, BertGenerationEncoder),
        (DebertaConfig, DebertaModel),
        (DPRConfig, DPRQuestionEncoder),
        (XLMProphetNetConfig, XLMProphetNetModel),
        (ProphetNetConfig, ProphetNetModel),
    ]
)

MODEL_FOR_PRETRAINING_MAPPING = OrderedDict(
    [
        # Model for pre-training mapping
        (LayoutLMConfig, LayoutLMForMaskedLM),
        (RetriBertConfig, RetriBertModel),
        (T5Config, T5ForConditionalGeneration),
        (DistilBertConfig, DistilBertForMaskedLM),
        (AlbertConfig, AlbertForPreTraining),
        (CamembertConfig, CamembertForMaskedLM),
        (XLMRobertaConfig, XLMRobertaForMaskedLM),
        (BartConfig, BartForConditionalGeneration),
        (FSMTConfig, FSMTForConditionalGeneration),
        (LongformerConfig, LongformerForMaskedLM),
        (RobertaConfig, RobertaForMaskedLM),
        (SqueezeBertConfig, SqueezeBertForMaskedLM),
        (BertConfig, BertForPreTraining),
        (OpenAIGPTConfig, OpenAIGPTLMHeadModel),
        (GPT2Config, GPT2LMHeadModel),
        (MobileBertConfig, MobileBertForPreTraining),
        (TransfoXLConfig, TransfoXLLMHeadModel),
        (XLNetConfig, XLNetLMHeadModel),
        (FlaubertConfig, FlaubertWithLMHeadModel),
        (XLMConfig, XLMWithLMHeadModel),
        (CTRLConfig, CTRLLMHeadModel),
        (ElectraConfig, ElectraForPreTraining),
        (LxmertConfig, LxmertForPreTraining),
        (FunnelConfig, FunnelForPreTraining),
    ]
)

MODEL_WITH_LM_HEAD_MAPPING = OrderedDict(
    [
        # Model with LM heads mapping
        (LayoutLMConfig, LayoutLMForMaskedLM),
        (T5Config, T5ForConditionalGeneration),
        (DistilBertConfig, DistilBertForMaskedLM),
        (AlbertConfig, AlbertForMaskedLM),
        (CamembertConfig, CamembertForMaskedLM),
        (XLMRobertaConfig, XLMRobertaForMaskedLM),
        (MarianConfig, MarianMTModel),
        (FSMTConfig, FSMTForConditionalGeneration),
        (BartConfig, BartForConditionalGeneration),
        (LongformerConfig, LongformerForMaskedLM),
        (RobertaConfig, RobertaForMaskedLM),
        (SqueezeBertConfig, SqueezeBertForMaskedLM),
        (BertConfig, BertForMaskedLM),
        (OpenAIGPTConfig, OpenAIGPTLMHeadModel),
        (GPT2Config, GPT2LMHeadModel),
        (MobileBertConfig, MobileBertForMaskedLM),
        (TransfoXLConfig, TransfoXLLMHeadModel),
        (XLNetConfig, XLNetLMHeadModel),
        (FlaubertConfig, FlaubertWithLMHeadModel),
        (XLMConfig, XLMWithLMHeadModel),
        (CTRLConfig, CTRLLMHeadModel),
        (ElectraConfig, ElectraForMaskedLM),
        (EncoderDecoderConfig, EncoderDecoderModel),
        (ReformerConfig, ReformerModelWithLMHead),
        (FunnelConfig, FunnelForMaskedLM),
    ]
)

MODEL_FOR_CAUSAL_LM_MAPPING = OrderedDict(
    [
        # Model for Causal LM mapping
        (CamembertConfig, CamembertForCausalLM),
        (XLMRobertaConfig, XLMRobertaForCausalLM),
        (RobertaConfig, RobertaForCausalLM),
        (BertConfig, BertLMHeadModel),
        (OpenAIGPTConfig, OpenAIGPTLMHeadModel),
        (GPT2Config, GPT2LMHeadModel),
        (TransfoXLConfig, TransfoXLLMHeadModel),
        (XLNetConfig, XLNetLMHeadModel),
        (
            XLMConfig,
            XLMWithLMHeadModel,
        ),  # XLM can be MLM and CLM => model should be split similar to BERT; leave here for now
        (CTRLConfig, CTRLLMHeadModel),
        (ReformerConfig, ReformerModelWithLMHead),
        (BertGenerationConfig, BertGenerationDecoder),
        (XLMProphetNetConfig, XLMProphetNetForCausalLM),
        (ProphetNetConfig, ProphetNetForCausalLM),
    ]
)

MODEL_FOR_MASKED_LM_MAPPING = OrderedDict(
    [
        # Model for Masked LM mapping
        (LayoutLMConfig, LayoutLMForMaskedLM),
        (DistilBertConfig, DistilBertForMaskedLM),
        (AlbertConfig, AlbertForMaskedLM),
        (BartConfig, BartForConditionalGeneration),
        (CamembertConfig, CamembertForMaskedLM),
        (XLMRobertaConfig, XLMRobertaForMaskedLM),
        (LongformerConfig, LongformerForMaskedLM),
        (RobertaConfig, RobertaForMaskedLM),
        (SqueezeBertConfig, SqueezeBertForMaskedLM),
        (BertConfig, BertForMaskedLM),
        (MobileBertConfig, MobileBertForMaskedLM),
        (FlaubertConfig, FlaubertWithLMHeadModel),
        (XLMConfig, XLMWithLMHeadModel),
        (ElectraConfig, ElectraForMaskedLM),
        (ReformerConfig, ReformerForMaskedLM),
        (FunnelConfig, FunnelForMaskedLM),
    ]
)

MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING = OrderedDict(
    [
        # Model for Seq2Seq Causal LM mapping
        (T5Config, T5ForConditionalGeneration),
        (PegasusConfig, PegasusForConditionalGeneration),
        (MarianConfig, MarianMTModel),
        (MBartConfig, MBartForConditionalGeneration),
        (BlenderbotConfig, BlenderbotForConditionalGeneration),
        (BartConfig, BartForConditionalGeneration),
        (FSMTConfig, FSMTForConditionalGeneration),
        (EncoderDecoderConfig, EncoderDecoderModel),
        (XLMProphetNetConfig, XLMProphetNetForConditionalGeneration),
        (ProphetNetConfig, ProphetNetForConditionalGeneration),
    ]
)

MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING = OrderedDict(
    [
        # Model for Sequence Classification mapping
        (DistilBertConfig, DistilBertForSequenceClassification),
        (AlbertConfig, AlbertForSequenceClassification),
        (CamembertConfig, CamembertForSequenceClassification),
        (XLMRobertaConfig, XLMRobertaForSequenceClassification),
        (BartConfig, BartForSequenceClassification),
        (LongformerConfig, LongformerForSequenceClassification),
        (RobertaConfig, RobertaForSequenceClassification),
        (SqueezeBertConfig, SqueezeBertForSequenceClassification),
        (BertConfig, BertForSequenceClassification),
        (XLNetConfig, XLNetForSequenceClassification),
        (MobileBertConfig, MobileBertForSequenceClassification),
        (FlaubertConfig, FlaubertForSequenceClassification),
        (XLMConfig, XLMForSequenceClassification),
        (ElectraConfig, ElectraForSequenceClassification),
        (FunnelConfig, FunnelForSequenceClassification),
        (DebertaConfig, DebertaForSequenceClassification),
        (GPT2Config, GPT2ForSequenceClassification),
        (OpenAIGPTConfig, OpenAIGPTForSequenceClassification),
        (ReformerConfig, ReformerForSequenceClassification),
    ]
)

MODEL_FOR_QUESTION_ANSWERING_MAPPING = OrderedDict(
    [
        # Model for Question Answering mapping
        (DistilBertConfig, DistilBertForQuestionAnswering),
        (AlbertConfig, AlbertForQuestionAnswering),
        (CamembertConfig, CamembertForQuestionAnswering),
        (BartConfig, BartForQuestionAnswering),
        (LongformerConfig, LongformerForQuestionAnswering),
        (XLMRobertaConfig, XLMRobertaForQuestionAnswering),
        (RobertaConfig, RobertaForQuestionAnswering),
        (SqueezeBertConfig, SqueezeBertForQuestionAnswering),
        (BertConfig, BertForQuestionAnswering),
        (XLNetConfig, XLNetForQuestionAnsweringSimple),
        (FlaubertConfig, FlaubertForQuestionAnsweringSimple),
        (MobileBertConfig, MobileBertForQuestionAnswering),
        (XLMConfig, XLMForQuestionAnsweringSimple),
        (ElectraConfig, ElectraForQuestionAnswering),
        (ReformerConfig, ReformerForQuestionAnswering),
        (FunnelConfig, FunnelForQuestionAnswering),
        (LxmertConfig, LxmertForQuestionAnswering),
    ]
)

MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING = OrderedDict(
    [
        # Model for Token Classification mapping
        (LayoutLMConfig, LayoutLMForTokenClassification),
        (DistilBertConfig, DistilBertForTokenClassification),
        (CamembertConfig, CamembertForTokenClassification),
        (FlaubertConfig, FlaubertForTokenClassification),
        (XLMConfig, XLMForTokenClassification),
        (XLMRobertaConfig, XLMRobertaForTokenClassification),
        (LongformerConfig, LongformerForTokenClassification),
        (RobertaConfig, RobertaForTokenClassification),
        (SqueezeBertConfig, SqueezeBertForTokenClassification),
        (BertConfig, BertForTokenClassification),
        (MobileBertConfig, MobileBertForTokenClassification),
        (XLNetConfig, XLNetForTokenClassification),
        (AlbertConfig, AlbertForTokenClassification),
        (ElectraConfig, ElectraForTokenClassification),
        (FlaubertConfig, FlaubertForTokenClassification),
        (FunnelConfig, FunnelForTokenClassification),
    ]
)

MODEL_FOR_MULTIPLE_CHOICE_MAPPING = OrderedDict(
    [
        # Model for Multiple Choice mapping
        (CamembertConfig, CamembertForMultipleChoice),
        (ElectraConfig, ElectraForMultipleChoice),
        (XLMRobertaConfig, XLMRobertaForMultipleChoice),
        (LongformerConfig, LongformerForMultipleChoice),
        (RobertaConfig, RobertaForMultipleChoice),
        (SqueezeBertConfig, SqueezeBertForMultipleChoice),
        (BertConfig, BertForMultipleChoice),
        (DistilBertConfig, DistilBertForMultipleChoice),
        (MobileBertConfig, MobileBertForMultipleChoice),
        (XLNetConfig, XLNetForMultipleChoice),
        (AlbertConfig, AlbertForMultipleChoice),
        (XLMConfig, XLMForMultipleChoice),
        (FlaubertConfig, FlaubertForMultipleChoice),
        (FunnelConfig, FunnelForMultipleChoice),
    ]
)

MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING = OrderedDict(
    [
        (BertConfig, BertForNextSentencePrediction),
        (MobileBertConfig, MobileBertForNextSentencePrediction),
    ]
)

AUTO_MODEL_PRETRAINED_DOCSTRING = r"""

        The model class to instantiate is selected based on the :obj:`model_type` property of the config object (either
        passed as an argument or loaded from :obj:`pretrained_model_name_or_path` if possible), or when it's missing,
        by falling back to using pattern matching on :obj:`pretrained_model_name_or_path`:

        List options

        The model is set in evaluation mode by default using ``model.eval()`` (so for instance, dropout modules are
        deactivated). To train the model, you should first set it back in training mode with ``model.train()``

        Args:
            pretrained_model_name_or_path:
                Can be either:

                    - A string with the `shortcut name` of a pretrained model to load from cache or download, e.g.,
                      ``bert-base-uncased``.
                    - A string with the `identifier name` of a pretrained model that was user-uploaded to our S3, e.g.,
                      ``dbmdz/bert-base-german-cased``.
                    - A path to a `directory` containing model weights saved using
                      :func:`~transformers.PreTrainedModel.save_pretrained`, e.g., ``./my_model_directory/``.
                    - A path or url to a `tensorflow index checkpoint file` (e.g, ``./tf_model/model.ckpt.index``). In
                      this case, ``from_tf`` should be set to :obj:`True` and a configuration object should be provided
                      as ``config`` argument. This loading path is slower than converting the TensorFlow checkpoint in
                      a PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.
            model_args (additional positional arguments, `optional`):
                Will be passed along to the underlying model ``__init__()`` method.
            config (:class:`~transformers.PretrainedConfig`, `optional`):
                Configuration for the model to use instead of an automatically loaded configuration. Configuration can
                be automatically loaded when:

                    - The model is a model provided by the library (loaded with the `shortcut name` string of a
                      pretrained model).
                    - The model was saved using :meth:`~transformers.PreTrainedModel.save_pretrained` and is reloaded
                      by supplying the save directory.
                    - The model is loaded by supplying a local directory as ``pretrained_model_name_or_path`` and a
                      configuration JSON file named `config.json` is found in the directory.
            state_dict (`Dict[str, torch.Tensor]`, `optional`):
                A state dictionary to use instead of a state dictionary loaded from saved weights file.

                This option can be used if you want to create a model from a pretrained configuration but load your own
                weights. In this case though, you should check if using
                :func:`~transformers.PreTrainedModel.save_pretrained` and
                :func:`~transformers.PreTrainedModel.from_pretrained` is not a simpler option.
            cache_dir (:obj:`str`, `optional`):
                Path to a directory in which a downloaded pretrained model configuration should be cached if the
                standard cache should not be used.
            from_tf (:obj:`bool`, `optional`, defaults to :obj:`False`):
                Load the model weights from a TensorFlow checkpoint save file (see docstring of
                ``pretrained_model_name_or_path`` argument).
            force_download (:obj:`bool`, `optional`, defaults to :obj:`False`):
                Whether or not to force the (re-)download of the model weights and configuration files, overriding the
                cached versions if they exist.
            resume_download (:obj:`bool`, `optional`, defaults to :obj:`False`):
                Whether or not to delete incompletely received files. Will attempt to resume the download if such a
                file exists.
            proxies (:obj:`Dict[str, str], `optional`):
                A dictionary of proxy servers to use by protocol or endpoint, e.g., :obj:`{'http': 'foo.bar:3128',
                'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
            output_loading_info(:obj:`bool`, `optional`, defaults to :obj:`False`):
                Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.
            local_files_only(:obj:`bool`, `optional`, defaults to :obj:`False`):
                Whether or not to only look at local files (e.g., not try downloading the model).
            revision(:obj:`str`, `optional`, defaults to :obj:`"main"`):
                The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
                git-based system for storing models and other artifacts on huggingface.co, so ``revision`` can be any
                identifier allowed by git.
            kwargs (additional keyword arguments, `optional`):
                Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
                :obj:`output_attentions=True`). Behaves differently depending on whether a ``config`` is provided or
                automatically loaded:

                    - If a configuration is provided with ``config``, ``**kwargs`` will be directly passed to the
                      underlying model's ``__init__`` method (we assume all relevant updates to the configuration have
                      already been done)
                    - If a configuration is not provided, ``kwargs`` will be first passed to the configuration class
                      initialization function (:func:`~transformers.PretrainedConfig.from_pretrained`). Each key of
                      ``kwargs`` that corresponds to a configuration attribute will be used to override said attribute
                      with the supplied ``kwargs`` value. Remaining keys that do not correspond to any configuration
                      attribute will be passed to the underlying model's ``__init__`` function.
"""


class AutoModel:
    r"""
    This is a generic model class that will be instantiated as one of the base model classes of the library when
    created with the :meth:`~transformers.AutoModel.from_pretrained` class method or the
    :meth:`~transformers.AutoModel.from_config` class methods.

    This class cannot be instantiated directly using ``__init__()`` (throws an error).
    """

    def __init__(self):
        raise EnvironmentError(
            "AutoModel is designed to be instantiated "
            "using the `AutoModel.from_pretrained(pretrained_model_name_or_path)` or "
            "`AutoModel.from_config(config)` methods."
        )

    @classmethod
    @replace_list_option_in_docstrings(MODEL_MAPPING, use_model_types=False)
    def from_config(cls, config):
        r"""
        Instantiates one of the base model classes of the library from a configuration.

        Note:
            Loading a model from its configuration file does **not** load the model weights. It only affects the
            model's configuration. Use :meth:`~transformers.AutoModel.from_pretrained` to load the model weights.

        Args:
            config (:class:`~transformers.PretrainedConfig`):
                The model class to instantiate is selected based on the configuration class:

                List options

        Examples::

            >>> from transformers import AutoConfig, AutoModel
            >>> # Download configuration from S3 and cache.
            >>> config = AutoConfig.from_pretrained('bert-base-uncased')
            >>> model = AutoModel.from_config(config)
        """
        if type(config) in MODEL_MAPPING.keys():
            return MODEL_MAPPING[type(config)](config)
        raise ValueError(
            "Unrecognized configuration class {} for this kind of AutoModel: {}.\n"
            "Model type should be one of {}.".format(
                config.__class__, cls.__name__, ", ".join(c.__name__ for c in MODEL_MAPPING.keys())
            )
        )

    @classmethod
    @replace_list_option_in_docstrings(MODEL_MAPPING)
    @add_start_docstrings(
        "Instantiate one of the base model classes of the library from a pretrained model.",
        AUTO_MODEL_PRETRAINED_DOCSTRING,
    )
    def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
        r"""

        Examples::

            >>> from transformers import AutoConfig, AutoModel

            >>> # Download model and configuration from S3 and cache.
            >>> model = AutoModel.from_pretrained('bert-base-uncased')

            >>> # Update configuration during loading
            >>> model = AutoModel.from_pretrained('bert-base-uncased', output_attentions=True)
            >>> model.config.output_attentions
            True

            >>> # Loading from a TF checkpoint file instead of a PyTorch model (slower)
            >>> config = AutoConfig.from_json_file('./tf_model/bert_tf_model_config.json')
            >>> model = AutoModel.from_pretrained('./tf_model/bert_tf_checkpoint.ckpt.index', from_tf=True, config=config)
        """
        config = kwargs.pop("config", None)
        if not isinstance(config, PretrainedConfig):
            config, kwargs = AutoConfig.from_pretrained(
                pretrained_model_name_or_path, return_unused_kwargs=True, **kwargs
            )

        if type(config) in MODEL_MAPPING.keys():
            return MODEL_MAPPING[type(config)].from_pretrained(
                pretrained_model_name_or_path, *model_args, config=config, **kwargs
            )
        raise ValueError(
            "Unrecognized configuration class {} for this kind of AutoModel: {}.\n"
            "Model type should be one of {}.".format(
                config.__class__, cls.__name__, ", ".join(c.__name__ for c in MODEL_MAPPING.keys())
            )
        )


class AutoModelForPreTraining:
    r"""
    This is a generic model class that will be instantiated as one of the model classes of the library---with the
    architecture used for pretraining this model---when created with the when created with the
    :meth:`~transformers.AutoModelForPreTraining.from_pretrained` class method or the
    :meth:`~transformers.AutoModelForPreTraining.from_config` class method.

    This class cannot be instantiated directly using ``__init__()`` (throws an error).
    """

    def __init__(self):
        raise EnvironmentError(
            "AutoModelForPreTraining is designed to be instantiated "
            "using the `AutoModelForPreTraining.from_pretrained(pretrained_model_name_or_path)` or "
            "`AutoModelForPreTraining.from_config(config)` methods."
        )

    @classmethod
    @replace_list_option_in_docstrings(MODEL_FOR_PRETRAINING_MAPPING, use_model_types=False)
    def from_config(cls, config):
        r"""
        Instantiates one of the model classes of the library---with the architecture used for pretraining this
        model---from a configuration.

        Note:
            Loading a model from its configuration file does **not** load the model weights. It only affects the
            model's configuration. Use :meth:`~transformers.AutoModelForPreTraining.from_pretrained` to load the model
            weights.

        Args:
            config (:class:`~transformers.PretrainedConfig`):
                The model class to instantiate is selected based on the configuration class:

                List options

        Examples::

            >>> from transformers import AutoConfig, AutoModelForPreTraining
            >>> # Download configuration from S3 and cache.
            >>> config = AutoConfig.from_pretrained('bert-base-uncased')
            >>> model = AutoModelForPreTraining.from_config(config)
        """
        if type(config) in MODEL_FOR_PRETRAINING_MAPPING.keys():
            return MODEL_FOR_PRETRAINING_MAPPING[type(config)](config)
        raise ValueError(
            "Unrecognized configuration class {} for this kind of AutoModel: {}.\n"
            "Model type should be one of {}.".format(
                config.__class__, cls.__name__, ", ".join(c.__name__ for c in MODEL_FOR_PRETRAINING_MAPPING.keys())
            )
        )

    @classmethod
    @replace_list_option_in_docstrings(MODEL_FOR_PRETRAINING_MAPPING)
    @add_start_docstrings(
        "Instantiate one of the model classes of the library---with the architecture used for pretraining this ",
        "model---from a pretrained model.",
        AUTO_MODEL_PRETRAINED_DOCSTRING,
    )
    def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
        r"""
        Examples::

            >>> from transformers import AutoConfig, AutoModelForPreTraining

            >>> # Download model and configuration from S3 and cache.
            >>> model = AutoModelForPreTraining.from_pretrained('bert-base-uncased')

            >>> # Update configuration during loading
            >>> model = AutoModelForPreTraining.from_pretrained('bert-base-uncased', output_attentions=True)
            >>> model.config.output_attentions
            True

            >>> # Loading from a TF checkpoint file instead of a PyTorch model (slower)
            >>> config = AutoConfig.from_json_file('./tf_model/bert_tf_model_config.json')
            >>> model = AutoModelForPreTraining.from_pretrained('./tf_model/bert_tf_checkpoint.ckpt.index', from_tf=True, config=config)
        """
        config = kwargs.pop("config", None)
        if not isinstance(config, PretrainedConfig):
            config, kwargs = AutoConfig.from_pretrained(
                pretrained_model_name_or_path, return_unused_kwargs=True, **kwargs
            )

        if type(config) in MODEL_FOR_PRETRAINING_MAPPING.keys():
            return MODEL_FOR_PRETRAINING_MAPPING[type(config)].from_pretrained(
                pretrained_model_name_or_path, *model_args, config=config, **kwargs
            )
        raise ValueError(
            "Unrecognized configuration class {} for this kind of AutoModel: {}.\n"
            "Model type should be one of {}.".format(
                config.__class__, cls.__name__, ", ".join(c.__name__ for c in MODEL_FOR_PRETRAINING_MAPPING.keys())
            )
        )


class AutoModelWithLMHead:
    r"""
    This is a generic model class that will be instantiated as one of the model classes of the library---with a
    language modeling head---when created with the when created with the
    :meth:`~transformers.AutoModelWithLMHead.from_pretrained` class method or the
    :meth:`~transformers.AutoModelWithLMHead.from_config` class method.

    This class cannot be instantiated directly using ``__init__()`` (throws an error).

    .. warning::

        This class is deprecated and will be removed in a future version. Please use
        :class:`~transformers.AutoModelForCausalLM` for causal language models,
        :class:`~transformers.AutoModelForMaskedLM` for masked language models and
        :class:`~transformers.AutoModelForSeq2SeqLM` for encoder-decoder models.
    """

    def __init__(self):
        raise EnvironmentError(
            "AutoModelWithLMHead is designed to be instantiated "
            "using the `AutoModelWithLMHead.from_pretrained(pretrained_model_name_or_path)` or "
            "`AutoModelWithLMHead.from_config(config)` methods."
        )

    @classmethod
    @replace_list_option_in_docstrings(MODEL_WITH_LM_HEAD_MAPPING, use_model_types=False)
    def from_config(cls, config):
        r"""
        Instantiates one of the model classes of the library---with a language modeling head---from a configuration.

        Note:
            Loading a model from its configuration file does **not** load the model weights. It only affects the
            model's configuration. Use :meth:`~transformers.AutoModelWithLMHead.from_pretrained` to load the model
            weights.

        Args:
            config (:class:`~transformers.PretrainedConfig`):
                The model class to instantiate is selected based on the configuration class:

                List options

        Examples::

            >>> from transformers import AutoConfig, AutoModelWithLMHead
            >>> # Download configuration from S3 and cache.
            >>> config = AutoConfig.from_pretrained('bert-base-uncased')
            >>> model = AutoModelWithLMHead.from_config(config)
        """
        warnings.warn(
            "The class `AutoModelWithLMHead` is deprecated and will be removed in a future version. Please use "
            "`AutoModelForCausalLM` for causal language models, `AutoModelForMaskedLM` for masked language models and "
            "`AutoModelForSeq2SeqLM` for encoder-decoder models.",
            FutureWarning,
        )
        if type(config) in MODEL_WITH_LM_HEAD_MAPPING.keys():
            return MODEL_WITH_LM_HEAD_MAPPING[type(config)](config)
        raise ValueError(
            "Unrecognized configuration class {} for this kind of AutoModel: {}.\n"
            "Model type should be one of {}.".format(
                config.__class__, cls.__name__, ", ".join(c.__name__ for c in MODEL_WITH_LM_HEAD_MAPPING.keys())
            )
        )

    @classmethod
    @replace_list_option_in_docstrings(MODEL_WITH_LM_HEAD_MAPPING)
    @add_start_docstrings(
        "Instantiate one of the model classes of the library---with a language modeling head---from a pretrained ",
        "model.",
        AUTO_MODEL_PRETRAINED_DOCSTRING,
    )
    def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
        r"""
        Examples::

            >>> from transformers import AutoConfig, AutoModelWithLMHead

            >>> # Download model and configuration from S3 and cache.
            >>> model = AutoModelWithLMHead.from_pretrained('bert-base-uncased')

            >>> # Update configuration during loading
            >>> model = AutoModelWithLMHead.from_pretrained('bert-base-uncased', output_attentions=True)
            >>> model.config.output_attentions
            True

            >>> # Loading from a TF checkpoint file instead of a PyTorch model (slower)
            >>> config = AutoConfig.from_json_file('./tf_model/bert_tf_model_config.json')
            >>> model = AutoModelWithLMHead.from_pretrained('./tf_model/bert_tf_checkpoint.ckpt.index', from_tf=True, config=config)
        """
        warnings.warn(
            "The class `AutoModelWithLMHead` is deprecated and will be removed in a future version. Please use "
            "`AutoModelForCausalLM` for causal language models, `AutoModelForMaskedLM` for masked language models and "
            "`AutoModelForSeq2SeqLM` for encoder-decoder models.",
            FutureWarning,
        )
        config = kwargs.pop("config", None)
        if not isinstance(config, PretrainedConfig):
            config, kwargs = AutoConfig.from_pretrained(
                pretrained_model_name_or_path, return_unused_kwargs=True, **kwargs
            )

        if type(config) in MODEL_WITH_LM_HEAD_MAPPING.keys():
            return MODEL_WITH_LM_HEAD_MAPPING[type(config)].from_pretrained(
                pretrained_model_name_or_path, *model_args, config=config, **kwargs
            )
        raise ValueError(
            "Unrecognized configuration class {} for this kind of AutoModel: {}.\n"
            "Model type should be one of {}.".format(
                config.__class__, cls.__name__, ", ".join(c.__name__ for c in MODEL_WITH_LM_HEAD_MAPPING.keys())
            )
        )


class AutoModelForCausalLM:
    r"""
    This is a generic model class that will be instantiated as one of the model classes of the library---with a causal
    language modeling head---when created with the when created with the
    :meth:`~transformers.AutoModelForCausalLM.from_pretrained` class method or the
    :meth:`~transformers.AutoModelForCausalLM.from_config` class method.

    This class cannot be instantiated directly using ``__init__()`` (throws an error).
    """

    def __init__(self):
        raise EnvironmentError(
            "AutoModelForCausalLM is designed to be instantiated "
            "using the `AutoModelForCausalLM.from_pretrained(pretrained_model_name_or_path)` or "
            "`AutoModelForCausalLM.from_config(config)` methods."
        )

    @classmethod
    @replace_list_option_in_docstrings(MODEL_FOR_CAUSAL_LM_MAPPING, use_model_types=False)
    def from_config(cls, config):
        r"""
        Instantiates one of the model classes of the library---with a causal language modeling head---from a
        configuration.

        Note:
            Loading a model from its configuration file does **not** load the model weights. It only affects the
            model's configuration. Use :meth:`~transformers.AutoModelForCausalLM.from_pretrained` to load the model
            weights.

        Args:
            config (:class:`~transformers.PretrainedConfig`):
                The model class to instantiate is selected based on the configuration class:

                List options

        Examples::

            >>> from transformers import AutoConfig, AutoModelForCausalLM
            >>> # Download configuration from S3 and cache.
            >>> config = AutoConfig.from_pretrained('gpt2')
            >>> model = AutoModelForCausalLM.from_config(config)
        """
        if type(config) in MODEL_FOR_CAUSAL_LM_MAPPING.keys():
            return MODEL_FOR_CAUSAL_LM_MAPPING[type(config)](config)
        raise ValueError(
            "Unrecognized configuration class {} for this kind of AutoModel: {}.\n"
            "Model type should be one of {}.".format(
                config.__class__, cls.__name__, ", ".join(c.__name__ for c in MODEL_FOR_CAUSAL_LM_MAPPING.keys())
            )
        )

    @classmethod
    @replace_list_option_in_docstrings(MODEL_FOR_CAUSAL_LM_MAPPING)
    @add_start_docstrings(
        "Instantiate one of the model classes of the library---with a causal language modeling head---from a "
        "pretrained model.",
        AUTO_MODEL_PRETRAINED_DOCSTRING,
    )
    def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
        r"""
        Examples::

            >>> from transformers import AutoConfig, AutoModelForCausalLM

            >>> # Download model and configuration from S3 and cache.
            >>> model = AutoModelForCausalLM.from_pretrained('gpt2')

            >>> # Update configuration during loading
            >>> model = AutoModelForCausalLM.from_pretrained('gpt2', output_attentions=True)
            >>> model.config.output_attentions
            True

            >>> # Loading from a TF checkpoint file instead of a PyTorch model (slower)
            >>> config = AutoConfig.from_json_file('./tf_model/gpt2_tf_model_config.json')
            >>> model = AutoModelForCausalLM.from_pretrained('./tf_model/gpt2_tf_checkpoint.ckpt.index', from_tf=True, config=config)
        """
        config = kwargs.pop("config", None)
        if not isinstance(config, PretrainedConfig):
            config, kwargs = AutoConfig.from_pretrained(
                pretrained_model_name_or_path, return_unused_kwargs=True, **kwargs
            )

        if type(config) in MODEL_FOR_CAUSAL_LM_MAPPING.keys():
            return MODEL_FOR_CAUSAL_LM_MAPPING[type(config)].from_pretrained(
                pretrained_model_name_or_path, *model_args, config=config, **kwargs
            )
        raise ValueError(
            "Unrecognized configuration class {} for this kind of AutoModel: {}.\n"
            "Model type should be one of {}.".format(
                config.__class__, cls.__name__, ", ".join(c.__name__ for c in MODEL_FOR_CAUSAL_LM_MAPPING.keys())
            )
        )


class AutoModelForMaskedLM:
    r"""
    This is a generic model class that will be instantiated as one of the model classes of the library---with a masked
    language modeling head---when created with the when created with the
    :meth:`~transformers.AutoModelForMaskedLM.from_pretrained` class method or the
    :meth:`~transformers.AutoModelForMaskedLM.from_config` class method.

    This class cannot be instantiated directly using ``__init__()`` (throws an error).
    """

    def __init__(self):
        raise EnvironmentError(
            "AutoModelForMaskedLM is designed to be instantiated "
            "using the `AutoModelForMaskedLM.from_pretrained(pretrained_model_name_or_path)` or "
            "`AutoModelForMaskedLM.from_config(config)` methods."
        )

    @classmethod
    @replace_list_option_in_docstrings(MODEL_FOR_MASKED_LM_MAPPING, use_model_types=False)
    def from_config(cls, config):
        r"""
        Instantiates one of the model classes of the library---with a masked language modeling head---from a
        configuration.

        Note:
            Loading a model from its configuration file does **not** load the model weights. It only affects the
            model's configuration. Use :meth:`~transformers.AutoModelForMaskedLM.from_pretrained` to load the model
            weights.

        Args:
            config (:class:`~transformers.PretrainedConfig`):
                The model class to instantiate is selected based on the configuration class:

                List options

        Examples::

            >>> from transformers import AutoConfig, AutoModelForMaskedLM
            >>> # Download configuration from S3 and cache.
            >>> config = AutoConfig.from_pretrained('bert-base-uncased')
            >>> model = AutoModelForMaskedLM.from_config(config)
        """
        if type(config) in MODEL_FOR_MASKED_LM_MAPPING.keys():
            return MODEL_FOR_MASKED_LM_MAPPING[type(config)](config)
        raise ValueError(
            "Unrecognized configuration class {} for this kind of AutoModel: {}.\n"
            "Model type should be one of {}.".format(
                config.__class__, cls.__name__, ", ".join(c.__name__ for c in MODEL_FOR_MASKED_LM_MAPPING.keys())
            )
        )

    @classmethod
    @replace_list_option_in_docstrings(MODEL_FOR_MASKED_LM_MAPPING)
    @add_start_docstrings(
        "Instantiate one of the model classes of the library---with a masked language modeling head---from a "
        "pretrained model.",
        AUTO_MODEL_PRETRAINED_DOCSTRING,
    )
    def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
        r"""
        Examples::

            >>> from transformers import AutoConfig, AutoModelForMaskedLM

            >>> # Download model and configuration from S3 and cache.
            >>> model = AutoModelForMaskedLM.from_pretrained('bert-base-uncased')

            >>> # Update configuration during loading
            >>> model = AutoModelForMaskedLM.from_pretrained('bert-base-uncased', output_attentions=True)
            >>> model.config.output_attentions
            True

            >>> # Loading from a TF checkpoint file instead of a PyTorch model (slower)
            >>> config = AutoConfig.from_json_file('./tf_model/bert_tf_model_config.json')
            >>> model = AutoModelForMaskedLM.from_pretrained('./tf_model/bert_tf_checkpoint.ckpt.index', from_tf=True, config=config)
        """
        config = kwargs.pop("config", None)
        if not isinstance(config, PretrainedConfig):
            config, kwargs = AutoConfig.from_pretrained(
                pretrained_model_name_or_path, return_unused_kwargs=True, **kwargs
            )

        if type(config) in MODEL_FOR_MASKED_LM_MAPPING.keys():
            return MODEL_FOR_MASKED_LM_MAPPING[type(config)].from_pretrained(
                pretrained_model_name_or_path, *model_args, config=config, **kwargs
            )
        raise ValueError(
            "Unrecognized configuration class {} for this kind of AutoModel: {}.\n"
            "Model type should be one of {}.".format(
                config.__class__, cls.__name__, ", ".join(c.__name__ for c in MODEL_FOR_MASKED_LM_MAPPING.keys())
            )
        )


class AutoModelForSeq2SeqLM:
    r"""
    This is a generic model class that will be instantiated as one of the model classes of the library---with a
    sequence-to-sequence language modeling head---when created with the when created with the
    :meth:`~transformers.AutoModelForSeq2SeqLM.from_pretrained` class method or the
    :meth:`~transformers.AutoModelForSeq2SeqLM.from_config` class method.

    This class cannot be instantiated directly using ``__init__()`` (throws an error).
    """

    def __init__(self):
        raise EnvironmentError(
            "AutoModelForSeq2SeqLM is designed to be instantiated "
            "using the `AutoModelForSeq2SeqLM.from_pretrained(pretrained_model_name_or_path)` or "
            "`AutoModelForSeq2SeqLM.from_config(config)` methods."
        )

    @classmethod
    @replace_list_option_in_docstrings(MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING, use_model_types=False)
    def from_config(cls, config):
        r"""
        Instantiates one of the model classes of the library---with a sequence-to-sequence language modeling
        head---from a configuration.

        Note:
            Loading a model from its configuration file does **not** load the model weights. It only affects the
            model's configuration. Use :meth:`~transformers.AutoModelForSeq2SeqLM.from_pretrained` to load the model
            weights.

        Args:
            config (:class:`~transformers.PretrainedConfig`):
                The model class to instantiate is selected based on the configuration class:

                List options

        Examples::

            >>> from transformers import AutoConfig, AutoModelForSeq2SeqLM
            >>> # Download configuration from S3 and cache.
            >>> config = AutoConfig.from_pretrained('t5')
            >>> model = AutoModelForSeq2SeqLM.from_config(config)
        """
        if type(config) in MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING.keys():
            return MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING[type(config)](config)
        raise ValueError(
            "Unrecognized configuration class {} for this kind of AutoModel: {}.\n"
            "Model type should be one of {}.".format(
                config.__class__,
                cls.__name__,
                ", ".join(c.__name__ for c in MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING.keys()),
            )
        )

    @classmethod
    @replace_list_option_in_docstrings(MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING)
    @add_start_docstrings(
        "Instantiate one of the model classes of the library---with a sequence-to-sequence language modeling "
        "head---from a pretrained model.",
        AUTO_MODEL_PRETRAINED_DOCSTRING,
    )
    def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
        r"""
        Examples::

            >>> from transformers import AutoConfig, AutoModelForSeq2SeqLM

            >>> # Download model and configuration from S3 and cache.
            >>> model = AutoModelForSeq2SeqLM.from_pretrained('t5-base')

            >>> # Update configuration during loading
            >>> model = AutoModelForSeq2SeqLM.from_pretrained('t5-base', output_attentions=True)
            >>> model.config.output_attentions
            True

            >>> # Loading from a TF checkpoint file instead of a PyTorch model (slower)
            >>> config = AutoConfig.from_json_file('./tf_model/t5_tf_model_config.json')
            >>> model = AutoModelForSeq2SeqLM.from_pretrained('./tf_model/t5_tf_checkpoint.ckpt.index', from_tf=True, config=config)
        """
        config = kwargs.pop("config", None)
        if not isinstance(config, PretrainedConfig):
            config, kwargs = AutoConfig.from_pretrained(
                pretrained_model_name_or_path, return_unused_kwargs=True, **kwargs
            )

        if type(config) in MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING.keys():
            return MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING[type(config)].from_pretrained(
                pretrained_model_name_or_path, *model_args, config=config, **kwargs
            )
        raise ValueError(
            "Unrecognized configuration class {} for this kind of AutoModel: {}.\n"
            "Model type should be one of {}.".format(
                config.__class__,
                cls.__name__,
                ", ".join(c.__name__ for c in MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING.keys()),
            )
        )


class AutoModelForSequenceClassification:
    r"""
    This is a generic model class that will be instantiated as one of the model classes of the library---with a
    sequence classification head---when created with the when created with the
    :meth:`~transformers.AutoModelForSequenceClassification.from_pretrained` class method or the
    :meth:`~transformers.AutoModelForSequenceClassification.from_config` class method.

    This class cannot be instantiated directly using ``__init__()`` (throws an error).
    """

    def __init__(self):
        raise EnvironmentError(
            "AutoModelForSequenceClassification is designed to be instantiated "
            "using the `AutoModelForSequenceClassification.from_pretrained(pretrained_model_name_or_path)` or "
            "`AutoModelForSequenceClassification.from_config(config)` methods."
        )

    @classmethod
    @replace_list_option_in_docstrings(MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING, use_model_types=False)
    def from_config(cls, config):
        r"""
        Instantiates one of the model classes of the library---with a sequence classification head---from a
        configuration.

        Note:
            Loading a model from its configuration file does **not** load the model weights. It only affects the
            model's configuration. Use :meth:`~transformers.AutoModelForSequenceClassification.from_pretrained` to load
            the model weights.

        Args:
            config (:class:`~transformers.PretrainedConfig`):
                The model class to instantiate is selected based on the configuration class:

                List options

        Examples::

            >>> from transformers import AutoConfig, AutoModelForSequenceClassification
            >>> # Download configuration from S3 and cache.
            >>> config = AutoConfig.from_pretrained('bert-base-uncased')
            >>> model = AutoModelForSequenceClassification.from_config(config)
        """
        if type(config) in MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING.keys():
            return MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING[type(config)](config)
        raise ValueError(
            "Unrecognized configuration class {} for this kind of AutoModel: {}.\n"
            "Model type should be one of {}.".format(
                config.__class__,
                cls.__name__,
                ", ".join(c.__name__ for c in MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING.keys()),
            )
        )

    @classmethod
    @replace_list_option_in_docstrings(MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING)
    @add_start_docstrings(
        "Instantiate one of the model classes of the library---with a sequence classification head---from a "
        "pretrained model.",
        AUTO_MODEL_PRETRAINED_DOCSTRING,
    )
    def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
        r"""
        Examples::

            >>> from transformers import AutoConfig, AutoModelForSequenceClassification

            >>> # Download model and configuration from S3 and cache.
            >>> model = AutoModelForSequenceClassification.from_pretrained('bert-base-uncased')

            >>> # Update configuration during loading
            >>> model = AutoModelForSequenceClassification.from_pretrained('bert-base-uncased', output_attentions=True)
            >>> model.config.output_attentions
            True

            >>> # Loading from a TF checkpoint file instead of a PyTorch model (slower)
            >>> config = AutoConfig.from_json_file('./tf_model/bert_tf_model_config.json')
            >>> model = AutoModelForSequenceClassification.from_pretrained('./tf_model/bert_tf_checkpoint.ckpt.index', from_tf=True, config=config)
        """
        config = kwargs.pop("config", None)
        if not isinstance(config, PretrainedConfig):
            config, kwargs = AutoConfig.from_pretrained(
                pretrained_model_name_or_path, return_unused_kwargs=True, **kwargs
            )

        if type(config) in MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING.keys():
            return MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING[type(config)].from_pretrained(
                pretrained_model_name_or_path, *model_args, config=config, **kwargs
            )
        raise ValueError(
            "Unrecognized configuration class {} for this kind of AutoModel: {}.\n"
            "Model type should be one of {}.".format(
                config.__class__,
                cls.__name__,
                ", ".join(c.__name__ for c in MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING.keys()),
            )
        )


class AutoModelForQuestionAnswering:
    r"""
    This is a generic model class that will be instantiated as one of the model classes of the library---with a
    question answering head---when created with the when created with the
    :meth:`~transformers.AutoModeForQuestionAnswering.from_pretrained` class method or the
    :meth:`~transformers.AutoModelForQuestionAnswering.from_config` class method.

    This class cannot be instantiated directly using ``__init__()`` (throws an error).
    """

    def __init__(self):
        raise EnvironmentError(
            "AutoModelForQuestionAnswering is designed to be instantiated "
            "using the `AutoModelForQuestionAnswering.from_pretrained(pretrained_model_name_or_path)` or "
            "`AutoModelForQuestionAnswering.from_config(config)` methods."
        )

    @classmethod
    @replace_list_option_in_docstrings(MODEL_FOR_QUESTION_ANSWERING_MAPPING, use_model_types=False)
    def from_config(cls, config):
        r"""
        Instantiates one of the model classes of the library---with a question answering head---from a configuration.

        Note:
            Loading a model from its configuration file does **not** load the model weights. It only affects the
            model's configuration. Use :meth:`~transformers.AutoModelForQuestionAnswering.from_pretrained` to load the
            model weights.

        Args:
            config (:class:`~transformers.PretrainedConfig`):
                The model class to instantiate is selected based on the configuration class:

                List options

        Examples::

            >>> from transformers import AutoConfig, AutoModelForQuestionAnswering
            >>> # Download configuration from S3 and cache.
            >>> config = AutoConfig.from_pretrained('bert-base-uncased')
            >>> model = AutoModelForQuestionAnswering.from_config(config)
        """
        if type(config) in MODEL_FOR_QUESTION_ANSWERING_MAPPING.keys():
            return MODEL_FOR_QUESTION_ANSWERING_MAPPING[type(config)](config)

        raise ValueError(
            "Unrecognized configuration class {} for this kind of AutoModel: {}.\n"
            "Model type should be one of {}.".format(
                config.__class__,
                cls.__name__,
                ", ".join(c.__name__ for c in MODEL_FOR_QUESTION_ANSWERING_MAPPING.keys()),
            )
        )

    @classmethod
    @replace_list_option_in_docstrings(MODEL_FOR_QUESTION_ANSWERING_MAPPING)
    @add_start_docstrings(
        "Instantiate one of the model classes of the library---with a question answering head---from a "
        "pretrained model.",
        AUTO_MODEL_PRETRAINED_DOCSTRING,
    )
    def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
        r"""
        Examples::

            >>> from transformers import AutoConfig, AutoModelForQuestionAnswering

            >>> # Download model and configuration from S3 and cache.
            >>> model = AutoModelForQuestionAnswering.from_pretrained('bert-base-uncased')

            >>> # Update configuration during loading
            >>> model = AutoModelForQuestionAnswering.from_pretrained('bert-base-uncased', output_attentions=True)
            >>> model.config.output_attentions
            True

            >>> # Loading from a TF checkpoint file instead of a PyTorch model (slower)
            >>> config = AutoConfig.from_json_file('./tf_model/bert_tf_model_config.json')
            >>> model = AutoModelForQuestionAnswering.from_pretrained('./tf_model/bert_tf_checkpoint.ckpt.index', from_tf=True, config=config)
        """
        config = kwargs.pop("config", None)
        if not isinstance(config, PretrainedConfig):
            config, kwargs = AutoConfig.from_pretrained(
                pretrained_model_name_or_path, return_unused_kwargs=True, **kwargs
            )

        if type(config) in MODEL_FOR_QUESTION_ANSWERING_MAPPING.keys():
            return MODEL_FOR_QUESTION_ANSWERING_MAPPING[type(config)].from_pretrained(
                pretrained_model_name_or_path, *model_args, config=config, **kwargs
            )

        raise ValueError(
            "Unrecognized configuration class {} for this kind of AutoModel: {}.\n"
            "Model type should be one of {}.".format(
                config.__class__,
                cls.__name__,
                ", ".join(c.__name__ for c in MODEL_FOR_QUESTION_ANSWERING_MAPPING.keys()),
            )
        )


class AutoModelForTokenClassification:
    r"""
    This is a generic model class that will be instantiated as one of the model classes of the library---with a token
    classification head---when created with the when created with the
    :meth:`~transformers.AutoModelForTokenClassification.from_pretrained` class method or the
    :meth:`~transformers.AutoModelForTokenClassification.from_config` class method.

    This class cannot be instantiated directly using ``__init__()`` (throws an error).
    """

    def __init__(self):
        raise EnvironmentError(
            "AutoModelForTokenClassification is designed to be instantiated "
            "using the `AutoModelForTokenClassification.from_pretrained(pretrained_model_name_or_path)` or "
            "`AutoModelForTokenClassification.from_config(config)` methods."
        )

    @classmethod
    @replace_list_option_in_docstrings(MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING, use_model_types=False)
    def from_config(cls, config):
        r"""
        Instantiates one of the model classes of the library---with a token classification head---from a configuration.

        Note:
            Loading a model from its configuration file does **not** load the model weights. It only affects the
            model's configuration. Use :meth:`~transformers.AutoModelForTokenClassification.from_pretrained` to load
            the model weights.

        Args:
            config (:class:`~transformers.PretrainedConfig`):
                The model class to instantiate is selected based on the configuration class:

                List options

        Examples::

            >>> from transformers import AutoConfig, AutoModelForTokenClassification
            >>> # Download configuration from S3 and cache.
            >>> config = AutoConfig.from_pretrained('bert-base-uncased')
            >>> model = AutoModelForTokenClassification.from_config(config)
        """
        if type(config) in MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING.keys():
            return MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING[type(config)](config)

        raise ValueError(
            "Unrecognized configuration class {} for this kind of AutoModel: {}.\n"
            "Model type should be one of {}.".format(
                config.__class__,
                cls.__name__,
                ", ".join(c.__name__ for c in MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING.keys()),
            )
        )

    @classmethod
    @replace_list_option_in_docstrings(MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING)
    @add_start_docstrings(
        "Instantiate one of the model classes of the library---with a token classification head---from a "
        "pretrained model.",
        AUTO_MODEL_PRETRAINED_DOCSTRING,
    )
    def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
        r"""
        Examples::

            >>> from transformers import AutoConfig, AutoModelForTokenClassification

            >>> # Download model and configuration from S3 and cache.
            >>> model = AutoModelForTokenClassification.from_pretrained('bert-base-uncased')

            >>> # Update configuration during loading
            >>> model = AutoModelForTokenClassification.from_pretrained('bert-base-uncased', output_attentions=True)
            >>> model.config.output_attentions
            True

            >>> # Loading from a TF checkpoint file instead of a PyTorch model (slower)
            >>> config = AutoConfig.from_json_file('./tf_model/bert_tf_model_config.json')
            >>> model = AutoModelForTokenClassification.from_pretrained('./tf_model/bert_tf_checkpoint.ckpt.index', from_tf=True, config=config)
        """
        config = kwargs.pop("config", None)
        if not isinstance(config, PretrainedConfig):
            config, kwargs = AutoConfig.from_pretrained(
                pretrained_model_name_or_path, return_unused_kwargs=True, **kwargs
            )

        if type(config) in MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING.keys():
            return MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING[type(config)].from_pretrained(
                pretrained_model_name_or_path, *model_args, config=config, **kwargs
            )

        raise ValueError(
            "Unrecognized configuration class {} for this kind of AutoModel: {}.\n"
            "Model type should be one of {}.".format(
                config.__class__,
                cls.__name__,
                ", ".join(c.__name__ for c in MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING.keys()),
            )
        )


class AutoModelForMultipleChoice:
    r"""
    This is a generic model class that will be instantiated as one of the model classes of the library---with a
    multiple choice classification head---when created with the when created with the
    :meth:`~transformers.AutoModelForMultipleChoice.from_pretrained` class method or the
    :meth:`~transformers.AutoModelForMultipleChoice.from_config` class method.

    This class cannot be instantiated directly using ``__init__()`` (throws an error).
    """

    def __init__(self):
        raise EnvironmentError(
            "AutoModelForMultipleChoice is designed to be instantiated "
            "using the `AutoModelForMultipleChoice.from_pretrained(pretrained_model_name_or_path)` or "
            "`AutoModelForMultipleChoice.from_config(config)` methods."
        )

    @classmethod
    @replace_list_option_in_docstrings(MODEL_FOR_MULTIPLE_CHOICE_MAPPING, use_model_types=False)
    def from_config(cls, config):
        r"""
        Instantiates one of the model classes of the library---with a multiple choice classification head---from a
        configuration.

        Note:
            Loading a model from its configuration file does **not** load the model weights. It only affects the
            model's configuration. Use :meth:`~transformers.AutoModelForMultipleChoice.from_pretrained` to load the
            model weights.

        Args:
            config (:class:`~transformers.PretrainedConfig`):
                The model class to instantiate is selected based on the configuration class:

                List options

        Examples::

            >>> from transformers import AutoConfig, AutoModelForMultipleChoice
            >>> # Download configuration from S3 and cache.
            >>> config = AutoConfig.from_pretrained('bert-base-uncased')
            >>> model = AutoModelForMultipleChoice.from_config(config)
        """
        if type(config) in MODEL_FOR_MULTIPLE_CHOICE_MAPPING.keys():
            return MODEL_FOR_MULTIPLE_CHOICE_MAPPING[type(config)](config)

        raise ValueError(
            "Unrecognized configuration class {} for this kind of AutoModel: {}.\n"
            "Model type should be one of {}.".format(
                config.__class__,
                cls.__name__,
                ", ".join(c.__name__ for c in MODEL_FOR_MULTIPLE_CHOICE_MAPPING.keys()),
            )
        )

    @classmethod
    @replace_list_option_in_docstrings(MODEL_FOR_MULTIPLE_CHOICE_MAPPING)
    @add_start_docstrings(
        "Instantiate one of the model classes of the library---with a multiple choice classification head---from a "
        "pretrained model.",
        AUTO_MODEL_PRETRAINED_DOCSTRING,
    )
    def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
        r"""
        Examples::

            >>> from transformers import AutoConfig, AutoModelForMultipleChoice

            >>> # Download model and configuration from S3 and cache.
            >>> model = AutoModelForMultipleChoice.from_pretrained('bert-base-uncased')

            >>> # Update configuration during loading
            >>> model = AutoModelForMultipleChoice.from_pretrained('bert-base-uncased', output_attentions=True)
            >>> model.config.output_attentions
            True

            >>> # Loading from a TF checkpoint file instead of a PyTorch model (slower)
            >>> config = AutoConfig.from_json_file('./tf_model/bert_tf_model_config.json')
            >>> model = AutoModelForMultipleChoice.from_pretrained('./tf_model/bert_tf_checkpoint.ckpt.index', from_tf=True, config=config)
        """
        config = kwargs.pop("config", None)
        if not isinstance(config, PretrainedConfig):
            config, kwargs = AutoConfig.from_pretrained(
                pretrained_model_name_or_path, return_unused_kwargs=True, **kwargs
            )

        if type(config) in MODEL_FOR_MULTIPLE_CHOICE_MAPPING.keys():
            return MODEL_FOR_MULTIPLE_CHOICE_MAPPING[type(config)].from_pretrained(
                pretrained_model_name_or_path, *model_args, config=config, **kwargs
            )

        raise ValueError(
            "Unrecognized configuration class {} for this kind of AutoModel: {}.\n"
            "Model type should be one of {}.".format(
                config.__class__,
                cls.__name__,
                ", ".join(c.__name__ for c in MODEL_FOR_MULTIPLE_CHOICE_MAPPING.keys()),
            )
        )


class AutoModelForNextSentencePrediction:
    r"""
    This is a generic model class that will be instantiated as one of the model classes of the library---with a
    multiple choice classification head---when created with the when created with the
    :meth:`~transformers.AutoModelForNextSentencePrediction.from_pretrained` class method or the
    :meth:`~transformers.AutoModelForNextSentencePrediction.from_config` class method.

    This class cannot be instantiated directly using ``__init__()`` (throws an error).
    """

    def __init__(self):
        raise EnvironmentError(
            "AutoModelForNextSentencePrediction is designed to be instantiated "
            "using the `AutoModelForNextSentencePrediction.from_pretrained(pretrained_model_name_or_path)` or "
            "`AutoModelForNextSentencePrediction.from_config(config)` methods."
        )

    @classmethod
    @replace_list_option_in_docstrings(MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING, use_model_types=False)
    def from_config(cls, config):
        r"""
        Instantiates one of the model classes of the library---with a multiple choice classification head---from a
        configuration.

        Note:
            Loading a model from its configuration file does **not** load the model weights. It only affects the
            model's configuration. Use :meth:`~transformers.AutoModelForNextSentencePrediction.from_pretrained` to load
            the model weights.

        Args:
            config (:class:`~transformers.PretrainedConfig`):
                The model class to instantiate is selected based on the configuration class:

                List options

        Examples::

            >>> from transformers import AutoConfig, AutoModelForNextSentencePrediction
            >>> # Download configuration from S3 and cache.
            >>> config = AutoConfig.from_pretrained('bert-base-uncased')
            >>> model = AutoModelForNextSentencePrediction.from_config(config)
        """
        if type(config) in MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING.keys():
            return MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING[type(config)](config)

        raise ValueError(
            "Unrecognized configuration class {} for this kind of AutoModel: {}.\n"
            "Model type should be one of {}.".format(
                config.__class__,
                cls.__name__,
                ", ".join(c.__name__ for c in MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING.keys()),
            )
        )

    @classmethod
    @replace_list_option_in_docstrings(MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING)
    @add_start_docstrings(
        "Instantiate one of the model classes of the library---with a multiple choice classification head---from a "
        "pretrained model.",
        AUTO_MODEL_PRETRAINED_DOCSTRING,
    )
    def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
        r"""
        Examples::

            >>> from transformers import AutoConfig, AutoModelForNextSentencePrediction

            >>> # Download model and configuration from S3 and cache.
            >>> model = AutoModelForNextSentencePrediction.from_pretrained('bert-base-uncased')

            >>> # Update configuration during loading
            >>> model = AutoModelForNextSentencePrediction.from_pretrained('bert-base-uncased', output_attentions=True)
            >>> model.config.output_attentions
            True

            >>> # Loading from a TF checkpoint file instead of a PyTorch model (slower)
            >>> config = AutoConfig.from_json_file('./tf_model/bert_tf_model_config.json')
            >>> model = AutoModelForNextSentencePrediction.from_pretrained('./tf_model/bert_tf_checkpoint.ckpt.index', from_tf=True, config=config)
        """
        config = kwargs.pop("config", None)
        if not isinstance(config, PretrainedConfig):
            config, kwargs = AutoConfig.from_pretrained(
                pretrained_model_name_or_path, return_unused_kwargs=True, **kwargs
            )

        if type(config) in MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING.keys():
            return MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING[type(config)].from_pretrained(
                pretrained_model_name_or_path, *model_args, config=config, **kwargs
            )

        raise ValueError(
            "Unrecognized configuration class {} for this kind of AutoModel: {}.\n"
            "Model type should be one of {}.".format(
                config.__class__,
                cls.__name__,
                ", ".join(c.__name__ for c in MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING.keys()),
            )
        )
